Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nexscope-ai/eCommerce-Skills --skill product-review-analysisgit clone --depth 1 https://github.com/nexscope-ai/eCommerce-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nexscope-ai/ecommerce-skills/product-review-analysis)<a href="https://agentmods.dev/skills/nexscope-ai/ecommerce-skills/product-review-analysis"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/product-review-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nexscope-ai/ecommerce-skills/product-review-analysis"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/product-review-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 14 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.02951 |
| Opus 5 | $0.00026 | $0.01476 |
| Sonnet 5 | $0.00010 | $0.00590 |
| Haiku 4.5 | $0.00005 | $0.00295 |
Grade A, and why
product-review-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Review Analysis ⭐
Transform customer reviews into actionable product and marketing intelligence. Extract insights, identify opportunities, optimize offerings.
Installation
npx skills add nexscope-ai/eCommerce-Skills --skill product-review-analysis -g
Usage Examples
Product improvement insights:
"Analyze reviews for my wireless headphones - what are customers complaining about most?"
Competitive review intelligence:
"Compare customer sentiment between my product and top 3 competitors from their reviews"
Feature development guidance:
"What features are customers requesting most in fitness tracker reviews?"
Core Capabilities
1. Sentiment Analysis & Classification
- Overall sentiment scoring and trend analysis
- Emotion detection and customer satisfaction measurement
- Review authenticity assessment and quality filtering
- Temporal sentiment tracking and pattern identification
2. Pain Point & Praise Pattern Analysis
- Systematic complaint categorization and frequency analysis
- Positive feedback theme identification and strength assessment
- Root cause analysis for customer dissatisfaction
- Success factor identification from positive reviews
3. Feature Request & Improvement Intelligence
- Customer-driven feature request extraction and prioritization
- Unmet need identification and market opportunity analysis
- Product development roadmap insights from customer feedback
- Competitive gap analysis from cross-brand review comparison
How It Works
Step 1: Review Collection & Sentiment Analysis
Comprehensive review data gathering and sentiment evaluation
Analyze customer feedback systematically:
- Collect and organize customer reviews from multiple platforms and sources
- Perform sentiment analysis and emotional tone assessment across review corpus
- Filter and categorize reviews by rating, recency, and authenticity indicators
- Identify review patterns, trends, and significant sentiment shifts over time
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 321 lines · 51 tokens per session scan A f685d0447460
product-review-analysis is a skill published in the GitHub repository nexscope-ai/eCommerce-Skills (914 stars, last pushed 16d ago), licensed MIT. It adds 51 tokens to every session and 2,951 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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